#load packages and read in data
library(tidyverse)
## ── Attaching packages ───────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.2.0 ✔ purrr 0.3.3
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 0.8.3 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## ── Conflicts ──────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
norep_noclim_df <- read_csv("norep_noclim.csv")
## Parsed with column specification:
## cols(
## b = col_double(),
## c = col_double(),
## year = col_double(),
## r = col_double(),
## f = col_double(),
## f_msy = col_double(),
## f_ratio = col_double(),
## f_ratio_err = col_double(),
## id = col_double(),
## r_0 = col_double(),
## error = col_double(),
## r_s = col_double()
## )
#alter dataset slightly, may be unnecessary, but we shall see
nr_nc_df <- norep_noclim_df %>%
mutate(parameters = paste0(paste0(paste0(paste0(paste0("r=", r), ", f="), f), ", err="), f_ratio_err)) %>%
group_by(id)
nr_nc_plot <- ggplot(nr_nc_df, aes(x = year, y = b, group = id)) +
geom_line(aes(color = r)) +
theme_bw()
nr_nc_plot
norep_clim_df <- read_csv("norep_clim.csv")
## Parsed with column specification:
## cols(
## b = col_double(),
## c = col_double(),
## year = col_double(),
## r = col_double(),
## f = col_double(),
## f_msy = col_double(),
## f_ratio = col_double(),
## f_ratio_err = col_double(),
## id = col_double(),
## r_0 = col_double(),
## error = col_double(),
## r_s = col_double()
## )
#alter dataset slightly, may be unnecessary, but we shall see
nr_c_df <- norep_clim_df %>%
mutate(parameters = paste0(paste0(paste0(paste0(paste0("r=", r), ", f="), f), ", err="), f_ratio_err)) %>%
group_by(id) %>%
filter(f!=0) %>%
filter(r_s < 0 & r_s >= -0.007)
nr_c_df$error <- as.factor(nr_c_df$error)
nr_c_plot <- ggplot(nr_c_df, aes(x = year, y = b, group = id)) +
geom_line(aes(color = r_0)) +
theme_bw() +
scale_color_continuous(low = "#079EDF", high = "#B8CE55", name = "Growth Rate") +
labs(title = "No Adaption + Climate", y = "Biomass", x = "Year") +
theme(axis.text.x=element_text(size=10), plot.title = element_text(hjust = 0.5, face = "bold", size = 15), axis.title.x = element_text(face = "bold"), axis.title.y = element_text(face = "bold"))
nr_c_plot
ggsave(filename="norep_clim.jpg", plot=last_plot())
## Saving 7 x 5 in image
rep5_clim_df <- read_csv("rep5_clim.csv")
## Parsed with column specification:
## cols(
## b = col_double(),
## c = col_double(),
## year = col_double(),
## r = col_double(),
## f = col_double(),
## f_msy = col_double(),
## f_ratio = col_double(),
## f_ratio_err = col_double(),
## id = col_double(),
## r_0 = col_double(),
## error = col_double(),
## r_s = col_double()
## )
#alter dataset slightly, may be unnecessary, but we shall see
r5_c_df <- rep5_clim_df %>%
group_by(id) %>%
filter(f!=0) %>%
filter(r_s < 0 & r_s >= -0.007) %>%
filter(error == 0.3)
r5_c_df$error <- as.factor(r5_c_df$error)
r5_c_df$r_s <- as.factor(r5_c_df$r_s)
r5_c_plot <- ggplot(r5_c_df, aes(x = year, y = b, group = id)) +
geom_line(aes(color = r_0)) +
theme_bw() +
scale_color_continuous(low = "#079EDF", high = "#B8CE55", name = "Growth Rate") +
labs(title = "5 Year Adaption + Climate", y = "Biomass", x = "Year") +
theme(axis.text.x=element_text(size=10), plot.title = element_text(hjust = 0.5, face = "bold", size = 15), axis.title.x = element_text(face = "bold"), axis.title.y = element_text(face = "bold"))
r5_c_plot
ggsave(filename="rep5_clim.jpg", plot=last_plot())
## Saving 7 x 5 in image
rep10_clim_df <- read_csv("rep10_clim.csv")
## Parsed with column specification:
## cols(
## b = col_double(),
## c = col_double(),
## year = col_double(),
## r = col_double(),
## f = col_double(),
## f_msy = col_double(),
## f_ratio = col_double(),
## f_ratio_err = col_double(),
## id = col_double(),
## r_0 = col_double(),
## error = col_double(),
## r_s = col_double()
## )
#alter dataset slightly, may be unnecessary, but we shall see
r10_c_df <- rep10_clim_df %>%
group_by(id) %>%
filter(f!=0) %>%
filter(r_s < 0 & r_s >= -0.007)%>%
filter(error == 0.3)
r10_c_df$error <- as.factor(r10_c_df$error)
r10_c_plot <- ggplot(r10_c_df, aes(x = year, y = b, group = id)) +
geom_line(aes(color = r_0)) +
theme_bw() +
scale_color_continuous(low = "#079EDF", high = "#B8CE55", name = "Growth Rate") +
labs(title = "10 Year Adaption + Climate", y = "Biomass", x = "Year") +
theme(axis.text.x=element_text(size=10), plot.title = element_text(hjust = 0.5, face = "bold", size = 15), axis.title.x = element_text(face = "bold"), axis.title.y = element_text(face = "bold"))
r10_c_plot
ggsave(filename="rep10_clim.jpg", plot=last_plot())
## Saving 7 x 5 in image
rep15_clim_df <- read_csv("rep15_clim.csv")
## Parsed with column specification:
## cols(
## b = col_double(),
## c = col_double(),
## year = col_double(),
## r = col_double(),
## f = col_double(),
## f_msy = col_double(),
## f_ratio = col_double(),
## f_ratio_err = col_double(),
## id = col_double(),
## r_0 = col_double(),
## error = col_double(),
## r_s = col_double()
## )
#alter dataset slightly, may be unnecessary, but we shall see
r15_c_df <- rep15_clim_df %>%
group_by(id) %>%
filter(f!=0) %>%
filter(r_s < 0 & r_s >= -0.007)%>%
filter(error == 0.3)
r15_c_df$error <- as.factor(r15_c_df$error)
r15_c_plot <- ggplot(r15_c_df, aes(x = year, y = b, group = id)) +
geom_line(aes(color = r_0)) +
theme_bw() +
scale_color_continuous(low = "#079EDF", high = "#B8CE55", name = "Growth Rate") +
labs(title = "15 Year Adaption + Climate", y = "Biomass", x = "Year") +
theme(axis.text.x=element_text(size=10), plot.title = element_text(hjust = 0.5, face = "bold", size = 15), axis.title.x = element_text(face = "bold"), axis.title.y = element_text(face = "bold"))
r15_c_plot
ggsave(filename="rep15_clim.jpg", plot=last_plot())
## Saving 7 x 5 in image
rep20_clim_df <- read_csv("rep20_clim.csv")
## Parsed with column specification:
## cols(
## b = col_double(),
## c = col_double(),
## year = col_double(),
## r = col_double(),
## f = col_double(),
## f_msy = col_double(),
## f_ratio = col_double(),
## f_ratio_err = col_double(),
## id = col_double(),
## r_0 = col_double(),
## error = col_double(),
## r_s = col_double()
## )
#alter dataset slightly, may be unnecessary, but we shall see
r20_c_df <- rep20_clim_df %>%
group_by(id) %>%
filter(f!=0) %>%
filter(r_s < 0 & r_s >= -0.007)%>%
filter(error == 0.3)
r20_c_df$error <- as.factor(r20_c_df$error)
r20_c_plot <- ggplot(r20_c_df, aes(x = year, y = b, group = id)) +
geom_line(aes(color = r_0)) +
theme_bw() +
scale_color_continuous(low = "#079EDF", high = "#B8CE55", name = "Growth Rate") +
labs(title = "20 Year Adaption + Climate", y = "Biomass", x = "Year") +
theme(axis.text.x=element_text(size=10), plot.title = element_text(hjust = 0.5, face = "bold", size = 15), axis.title.x = element_text(face = "bold"), axis.title.y = element_text(face = "bold"))
r20_c_plot
ggsave(filename="rep20_clim.jpg", plot=last_plot())
## Saving 7 x 5 in image
rep10_noclim_df <- read_csv("rep10_noclim.csv")
## Parsed with column specification:
## cols(
## b = col_double(),
## c = col_double(),
## year = col_double(),
## r = col_double(),
## f = col_double(),
## f_msy = col_double(),
## f_ratio = col_double(),
## f_ratio_err = col_double(),
## id = col_double(),
## r_0 = col_double(),
## error = col_double(),
## r_s = col_double()
## )
#alter dataset slightly, may be unnecessary, but we shall see
r10_nc_df <- rep10_noclim_df %>%
group_by(id) %>%
mutate(initial_r = ifelse(r <= .3, "slow-growing", ifelse(r >.3 & r <= .5, "medium-growing", "fast-growing"))) %>%
filter(f>=.1)
r10_nc_plot <- ggplot(r10_nc_df, aes(x = year, y = b, group = id)) +
geom_line(aes(color = r)) +
theme_bw() +
scale_color_continuous(low = "#079EDF", high = "#B8CE55", name = "Growth Rate") +
labs(title = "10 Year Adaption, No Climate", y = "Biomass", x = "Year") +
theme(axis.text.x=element_text(size=10), plot.title = element_text(hjust = 0.5, face = "bold", size = 15), axis.title.x = element_text(face = "bold"), axis.title.y = element_text(face = "bold"))
r10_nc_plot
ggsave(filename="rep10_noclim.jpg", plot=last_plot())
## Saving 7 x 5 in image